A typical skip-layer learning architecture is shown in Fig. REF (c), which is adopted in [1]}, [2]}, [3]}.
Rather than training multiple parallel streams on multiple scaled input images, skip-layer network learns multi-scale features “inside” a primary stream.
Multi-scale responses are learned from different layers with increasingly larger receptive fields and downsampling ratios,
and these responses are then concatenated together for outputting final saliency.
